The Resource Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource)
Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource)
Resource Information
The item Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
- Summary
- This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.--
- Language
- eng
- Edition
- 1st ed. 2020.
- Extent
- 1 online resource
- Contents
-
- Introduction to Robot Introspection
- Nonparametric Bayesian Modeling of Multimodal Time Series
- Incremental Learning Robot Complex Task Representation and Identification
- Nonparametric Bayesian Method for Robot Anomaly Monitoring
- Nonparametric Bayesian Method for Robot Anomaly Diagnose
- Learning Policy for Robot Anomaly Recovery based on Robot
- Isbn
- 9789811562631
- Label
- Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
- Title
- Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
- Statement of responsibility
- by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
- Language
- eng
- Summary
- This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.--
- Assigning source
- Provided by publisher
- http://library.link/vocab/creatorName
- Zhou, Xuefeng
- Image bit depth
- 0
- Literary form
- non fiction
- Nature of contents
- dictionaries
- http://library.link/vocab/relatedWorkOrContributorName
-
- Wu, Hongmin
- Rojas, Juan
- Xu, Zhihao
- Li, Shuai
- Series statement
- Open Access e-Books
- http://library.link/vocab/subjectName
-
- Robotics
- Automation
- Statistics
- Control engineering
- Mechatronics
- Machine learning
- Mathematical models
- Label
- Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource)
- Antecedent source
- mixed
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- not applicable
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- Introduction to Robot Introspection -- Nonparametric Bayesian Modeling of Multimodal Time Series -- Incremental Learning Robot Complex Task Representation and Identification -- Nonparametric Bayesian Method for Robot Anomaly Monitoring -- Nonparametric Bayesian Method for Robot Anomaly Diagnose -- Learning Policy for Robot Anomaly Recovery based on Robot
- Control code
- 978-981-15-6263-1
- Dimensions
- unknown
- Edition
- 1st ed. 2020.
- Extent
- 1 online resource
- File format
- multiple file formats
- Form of item
-
- online
- electronic
- Isbn
- 9789811562631
- Level of compression
- uncompressed
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Quality assurance targets
- absent
- Reformatting quality
- access
- Specific material designation
- remote
- System control number
- (OCoLC)1182513908
- Label
- Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource)
- Antecedent source
- mixed
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- not applicable
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- Introduction to Robot Introspection -- Nonparametric Bayesian Modeling of Multimodal Time Series -- Incremental Learning Robot Complex Task Representation and Identification -- Nonparametric Bayesian Method for Robot Anomaly Monitoring -- Nonparametric Bayesian Method for Robot Anomaly Diagnose -- Learning Policy for Robot Anomaly Recovery based on Robot
- Control code
- 978-981-15-6263-1
- Dimensions
- unknown
- Edition
- 1st ed. 2020.
- Extent
- 1 online resource
- File format
- multiple file formats
- Form of item
-
- online
- electronic
- Isbn
- 9789811562631
- Level of compression
- uncompressed
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Quality assurance targets
- absent
- Reformatting quality
- access
- Specific material designation
- remote
- System control number
- (OCoLC)1182513908
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Nonparametric-Bayesian-Learning-for-Collaborative/tAphs4XjdlY/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Nonparametric-Bayesian-Learning-for-Collaborative/tAphs4XjdlY/">Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Nonparametric-Bayesian-Learning-for-Collaborative/tAphs4XjdlY/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Nonparametric-Bayesian-Learning-for-Collaborative/tAphs4XjdlY/">Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li, (electronic resource)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
